Emily K. Lada

44 total papers · 450 total citations
26 papers, 326 citations indexed

About

Emily K. Lada is a scholar working on Management Science and Operations Research, Statistics, Probability and Uncertainty and Statistics and Probability. According to data from OpenAlex, Emily K. Lada has authored 26 papers receiving a total of 326 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Management Science and Operations Research, 11 papers in Statistics, Probability and Uncertainty and 8 papers in Statistics and Probability. Recurrent topics in Emily K. Lada's work include Simulation Techniques and Applications (18 papers), Advanced Statistical Process Monitoring (8 papers) and Statistical Methods and Bayesian Inference (4 papers). Emily K. Lada is often cited by papers focused on Simulation Techniques and Applications (18 papers), Advanced Statistical Process Monitoring (8 papers) and Statistical Methods and Bayesian Inference (4 papers). Emily K. Lada collaborates with scholars based in United States. Emily K. Lada's co-authors include James R. Wilson, Natalie M. Steiger, Jeffrey A. Joines, Michael E. Kuhl, Christos Alexopoulos, David Goldsman, Ali Tafazzoli, Lisa Mauck Weiland, James R. Wilson and Ralph C. Smith and has published in prestigious journals such as European Journal of Operational Research, Journal of materials research/Pratt's guide to venture capital sources and Smart Materials and Structures.

In The Last Decade

Emily K. Lada

24 papers receiving 307 citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Emily K. Lada 203 120 80 55 27 26 326
Faker Zouaoui 222 1.1× 134 1.1× 62 0.8× 31 0.6× 55 2.0× 10 306
B.W. Schmeiser 145 0.7× 39 0.3× 29 0.4× 37 0.7× 20 0.7× 17 359
Bahar Biller 168 0.8× 44 0.4× 45 0.6× 19 0.3× 116 4.3× 25 340
Michael Jong Kim 84 0.4× 113 0.9× 55 0.7× 78 1.4× 21 0.8× 23 335
Chih-Chung Lo 139 0.7× 25 0.2× 27 0.3× 46 0.8× 91 3.4× 23 354
Roberto Szechtman 149 0.7× 55 0.5× 20 0.3× 33 0.6× 25 0.9× 32 354
Keebom Kang 180 0.9× 84 0.7× 83 1.0× 41 0.7× 68 2.5× 37 292
S.D. Hill 171 0.8× 41 0.3× 28 0.3× 72 1.3× 35 1.3× 37 322
Susan R. Hunter 274 1.3× 103 0.9× 11 0.1× 29 0.5× 20 0.7× 30 362
Linmin Hu 28 0.1× 115 1.0× 74 0.9× 42 0.8× 78 2.9× 45 308

Countries citing papers authored by Emily K. Lada

Since Specialization
Citations

This map shows the geographic impact of Emily K. Lada's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Emily K. Lada with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emily K. Lada more than expected).

Fields of papers citing papers by Emily K. Lada

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Emily K. Lada. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Emily K. Lada. The network helps show where Emily K. Lada may publish in the future.

Co-authorship network of co-authors of Emily K. Lada

This figure shows the co-authorship network connecting the top 25 collaborators of Emily K. Lada. A scholar is included among the top collaborators of Emily K. Lada based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Emily K. Lada. Emily K. Lada is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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